QoE based energy reduction by controllin

2013 22nd ITC Specialist Seminar on Energy Efficient and Green Networking (SSEEGN)

QoE-Based Energy Reduction by Controlling the 3G
Cellular Data Traffic on the Smartphone
Selim Ickin

Katarzyna Wac

Markus Fiedler

School of Computing
Blekinge Institute of Technology
Karlskrona, SWEDEN
Email: selim.ickin@bth.se

Institute of Services Science
University of Geneva
Geneva, SWITZERLAND
Email: katarzyna.wac@unige.ch

School of Computing

Blekinge Institute of Technology
Karlskrona, SWEDEN
Email: markus.fiedler@bth.se

Abstract—One of the most influencing factors on the overall
end-user perceived quality from applications and services, i.e.,
QoE, running on the smartphones is their limited battery life.
Particular cloud-based applications/services on the smartphone
with a constrained battery life might consume high energy even
when the smartphone is in screen-OFF state. The cellular radio
module of the smartphone is one of the most power-consuming
components, which depends on the running applications’ information polling characteristics that eventually cause the radio module
to toggle occasionally between the cellular data energy states even
during a sleep state.
In this paper, we investigate the energy consumption of a set
of applications that tend to retain up-to-date information via
aggressive polling patterns. We show that limiting the network
traffic and increasing the resource utilization efficiency amongst
the applications and services can highly reduce the total energy
consumption. We control the network activity of a smartphone

with different cellular data-enabled and data-disabled durations
at the screen-OFF state. First, we run controlled-lab energy
measurements to have a ground truth on the power consumption
patterns of a set of cloud-based popular applications/services;
and next we conduct a subjective study with our proposed
solution (ExpCO2), to understand first the user behaviour on the
smartphone and then present how the reduced polling intervals
of applications and notifications influence the end-user perceived
quality. We indicate that ExpCO2 has a potential to save energy.

I.

I NTRODUCTION

The user perceived quality of the mobile applications depends on the total energy consumption of the mobile handheld
terminals, as the duration of user service time depends on the
lifetime of the capacity-limited batteries [1]. There are huge
efforts in industry and in academia dedicated to improve the
battery life, however those efforts remain isolated on specific
particular domains, e.g., software, hardware, and radio network

without consideration of the other domains [2]. The software
domain needs responsibility in energy-efficient application and
service development, otherwise might cause lower end-user
perceived quality, e.g., Quality of Experience (QoE), resulting
in users giving up using particular apps [4]. An energy-efficient
app is described in [2] as an app that is adopted to the
characteristics of the cellular network, while minimizing the
number of required connections and reducing the total connection duration to the network. The effectiveness of a system
is achieved when there is an appropriate balance between
the functionality [9] (a set of actions/services offered to the
users [8]), and the usability (degree by which the system can be
used efficiently and properly performing goals [8]). In order for

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13

the application/service to lead to a high user-perceived quality,
the cloud-based application developers need to reconsider their
designs to minimize energy consumption and maximize QoE.

As the radio cellular data module is one of the most powerconsuming components of the smartphone [11], minimizing
the energy consumption can be achieved through controlling
directly the cellular data interface.
The cloud-based mobile applications rely on the Internet
connectivity in order to keep their contents up-to-date and
present them to the users through notifications. Therefore, the
cloud-based applications require toggling the cellular radio
module to the active state occasionally to transmit/receive data
during the update intervals. In order to reduce the number of
oscillations between the states, the radio module preserves its
state for a certain amount of duration after being toggled ON,
even if the flow of packets in a user session is completed [2].
Therefore, upon activation, the radio module consumes power
at least for a constant period of time. As it is common to run
a high number of cloud-based applications in the background
simultaneously, high number of requests for toggling the radio
module to the full active (i.e., the most power-consuming)
state asynchronously would toggle the radio interface ON
and OFF at different times, which in sum might increase the
total energy consumption. However, a mechanism that can

control the cellular network interface of the smartphone in
a way to toggle the cellular data interface ON and OFF at
particular periodicity with various durations would let all the
applications’ network activities to be accomplished at fixed and
predefined time intervals. In other words, this would shift small
bursts that are spaced out with some intervals into rather fewer
number of large bursts [2]. We hypothesize that this might
reduce the total energy consumption; we build the study in
this paper on this basis. As a drawback, this proposal might
in parallel reduce the end-user perceived QoE as the end-users
are notified by the updated information with some latency.
Controlling, i.e., in the sense of enabling / disabling,
the cellular data interface might reduce the overall perceived
quality due to the increased burst size of data and latency
during the polling of the content, given that the users are
using these apps interactively. However, this might be possible when the users are not interacting actively with the
smartphone, i.e., basically when the smartphone screen is in
the OFF state. In this case, the applications are expected to
provide notifications to the user using pull/push functionalities.
Some users might be frequently checking their phone for new

2013 22nd ITC Specialist Seminar on Energy Efficient and Green Networking (SSEEGN)

emails, even as an addiction [12], while some other users do it
less often. Indeed, bombardment of notifications to users can
keep people busy checking them, while preventing them to
accomplish more important tasks, and eventually reducing their
productivity. Therefore, reducing the number of interruptions
caused by occasional notifications as a result of occasional
network activities, e.g., pull and push functionalities, would
possibly increase the focus on the task at hand and productivity.
Dawidow in [15] claims that the users should control tools to
accomplish tasks rather than letting the tools control the users,
and our research contributes to that.
Our main goal in this study is to reduce the total energy
consumption of a smartphone during its screen-OFF state,
while investigating its influence on information polling latency
on the end-user perceived QoE. We hypothesize that the
background running applications information update intervals
do not highly influence the user experience when the screen

is in OFF state. The research contribution of this paper is
as follows. In contrast with the previous related studies, we
propose a power saving software that can save energy by
controlling the cellular data interface in screen-OFF state,
while keeping the end-user perceived quality unchanged. The
proposed software does not require any major changes in the
network stack or in the OS, and can be easily installed to most
of the Android OS based smartphones.
The paper is structured as follows. The background information on the energy states of cellular radio module of
the smartphone is reviewed in Section II. The related work
regarding earlier proposed solutions to control the energy states
of the radio module as well as the work done to reveal
the disadvantages of notifying users with interruptions are
is discussed in Section III. The measurement data and the
experiment methodology are given in Section IV. We introduce
our two applications that are used to understand the power
consumption characteristics of a set of network activities; and
to understand the end-user perceived quality on the reduced
network activities. The results of the in-lab ground truth energy
ExpCO2 measurements are given in Section V. Next, the

details of the latter subjective study is presented in Section VI.
Section VII confers the subjective study results. We discuss
on the overall results and present the limitations of the study
in Section VIII. The paper is concluded and future work is
presented in Section IX.
II.

3G C ELLULAR R ADIO P OWER C ONSUMPTION

The cellular radio is designed to minimize the energy
consumption such that it transits between the most powerconsuming state to a less power-consuming state depending
on the network traffic activity. Radio Resource Control (RRC)
Protocol in Universal Mobile Telecommunications System
(UMTS) [3] has a state machine with three states that has
different power consumptions, and these states depend on
the data rate and the uplink/downlink queue sizes [17]. The
Idle state is assumed when no radio resource is allocated, no
connection between User Equipment (UE), e.g., smartphone,
and Radio Network Controller (RNC) is established, and the
UE cannot transfer any data. The DCH state is assumed when

UE-to-RNC connection is established, and the UE is allocated
with the dedicated DCH transport channels for both downlink
and uplink with the highest bandwidth and power. The FACH

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14

is the state for very low data throughput rate application
requirements, and in FACH, the RRC connection is established
but no channel is associated. The power consumption decreases
in the order of DCH, FACH, and Idle [2]. The cellular radio
module transits from DCH to Idle state first by transiting into
an intermediate state (FACH) depending on the tail time (can
last up to 15 seconds [2]), which depends on the network
provider, the traffic volume, or the smartphone manufacturer.
The amount of the web content to be polled and the connection
speed are important factors for the energy consumption during
the polling periods. In addition, there exists a delay between
the energy state transitions, which are due to the high number

of control messages being transmitted and received between
UE and the RRC.
III.

R ELATED W ORK

The power consumption of smartphones have been analyzed many times in previous works [19]. Some important
work so far are related to leveraging the 3rd Generation Partnership Project (3GPP) standards by proposing fast-dormancy.
This is basically forcing the transitions such as DCH to Idle
or FACH to Idle without waiting for the inactivity timeout
periods. Tail Optimization Protocol (TOP) is proposed by
Qian et al [5] to minimize the timeout periods, i.e., inactivity
times, between the states by invoking the fast dormancy
support. On the other hand, according to a research done
by AT&T [2], for particular apps, reducing the tail timer by
three seconds reduce the resource usage by 40 %, but increase
the number of state promotions by 31 %. In order to reduce
the initial latencies, it is claimed by Gerber et al [2] that
pipelined Hypertext Transfer Protocol (HTTP) requests into
a single Transport Control Protocol (TCP) connection rather

than sequential HTTP requests. AT&T diagnosed the problem
via app profiler, bundled the scatter-burst transmission of small
packets into a single transmission, and achieved 40 % energy
saving. The missing part in the research is how the suggested
solution influences the end-user perceived QoE.
There are Power Manager apps available in the app stores
and do user preference-based control over the smartphone
components and services to reduce energy consumption, automatically, however most of them are not used as their
setting requirements are sophisticated. Notification Center is
an app [7] that can control the application notifications based
on user preferences without disturbing the user much. Android
Jelly Bean provides users to toggle the “show notifications”
ON and OFF. There exist apps [13] such as “Addons Detected” that identifies the apps that work with push notification
services. “AwayFind” [20] is another app that notifies the
user when email has been received from preferred senders.
Although these solutions can prevent the users to receive
undesired notifications and minimize user disturbances, they do
not save radio power as the occasional network activities from
the apps are still granted. There are professional apps such as
PowerTutor [14]. The latter is used in research, and it presents
the power consumption of all available components and the
running applications to the users and the application developers. It works accurately only on particular phones such as HTC
G1/G2, on the other hand, it is highly resource-consuming as
it keeps the CPU busy to sample current values with high
precision. Ferreira et al investigated the power consumption of
smartphones and battery charging (battery connected to power

2013 22nd ITC Specialist Seminar on Energy Efficient and Green Networking (SSEEGN)

ï1

Idle

FACH

DCH

CCDF

10

ï2

10

0

www.bbc.com
www.cnn.com
www.slashdot.org
www.csv.com
PING (dedicated server IP)
200
400
600

800

1000

Power Consumption (mW)

1200

1400

1600

Fig. 1. Example of the power consumption of different states of the cellular
radio module for a single Ping execution with RTT = 1429 ms.

Fig. 2. Power consumption patterns for a set of applications. Black solid
line shows the approximate trend.

source) patterns of users through an app [10], and suggest
solutions to reduce waste of energy during overcharging periods, and also to reduce the damage the battery can get
via overheating/overvoltage. The most important difference
between the related work and ours is that we developed a
power saving toolkit, ExpCo2, conducted ground truth power
performance measures, and employed a subjective study in
order to assess the efficiency of the tool. ExpCO2 reduces
the total number of network activities by extending the Idle
periods. This prevents the user to be interrupted with push/poll
notifications of cloud-based apps, and also aims at minimizing
the total energy consumption and the network utilization.

recorded the Round Trip Time (RTT); and MPMT recorded the
average power consumption during the ping execution to an IP
address of a computer at BTH campus (to avoid any extra DNS
resolution latency). MPMT provided 3.7V to the smartphone
and measures the average power consumption with a default
sampling rate of 5 kHz

IV.

M ETHODOLOGY AND M EASUREMENT DATA

We have developed two different apps for the purpose of
this paper. The first app developed for the work presented in
this paper is used as a test software to understand the energy
states of the cellular radio module through ground truth measurements via the Monsoon Mobile Power Monitoring Tool
(MPMT) [6] during the screen-OFF state of the smartphone.
Our aim is to obtain background knowledge on the power
consumption changes amongst different cellular radio states.
As the second app, the ExpCO2 software is developed and
used in a subjective study to record a set of QoE metrics on
the users’ own smartphones.
A. Test Software and the MPMT-based Measurements
We developed a test software that initiates a set of commands, such as ping or web page download. By using MPMT,
we conducted in-lab energy measurements with the Samsung
Galaxy S with Lithium-ion battery. During the smartphone’s
sleep state, i.e., screen-OFF state, a set of echo request
messages with 30 seconds inter-departing time, are sent to
a dedicated server located at the BTH University campus in
Karlskrona, Sweden. The reason for 30 s inter-departing time
was a repeatability, i.e., waiting for the cellular radio to transit
back to Idle state, as each complete experiment repetition
initiates and ends at the Idle state. During the experiments,
the screen (backlight display), WiFi, GPS, Bluetooth, Audio,
Camera, and unrelated apps were OFF. This way, we prevent
additional power consumption caused by other components,
which made it possible to focus exclusively on the cellular
radio’s power consumption. During the experiments, we investigate the transition latency between the energy states and the
inactivity timeout periods amongst the three energy states of
the cellular radio module of the smartphone. The test software

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A transmission of only one echo request packet can wake
up the radio module during the sleep state of the smartphone.
It is possible to understand the minimum duration of a radio
module in the active state, knowing via “tcpdump” that there is
no further traffic after the reply message being received back.
As a representative example from one test run (see Fig. 1),
even during one RTT, e.g., 1429 ms (high value due to the
first packet effect) of a 64 Bytes ICMP packet, the duration
when the radio module state is not Idle, is roughly more than
10 seconds. Observe that the solid black line is the Simple
Moving Average (SMA) [23] with window size of 0.5 s, i.e.,
smoothing over 2500 power data samples. Thereby, the energy
is wasted during the remaining duration, e.g., Idle-To-DCH and
FACH-To-Idle periods. In addition, not all the time spent at
the DCH states are due to the data activity, rather due to the
inactivity timeout periods.
We further investigate the energy consumption during
web page download for a set of website URL’s. This
time, the test software is instructed to send GET messages
to popular website URL’s and then downloads the contents of the websites: cnn.com (news); (ii) bbc.com (news);
(iii) slashdot.org (RSS feed); (iv) cvs.com (retail). The test software records the total amount of download time and MPMT
records the power consumption similar to the ping experiments. We repeated these experiments in the lab every
45 seconds, i.e., a time period so that the radio module is
switched completely to the Idle state, before the next network
activity starts. In total, a minimum 35 iterations with 45 seconds time gap were conducted and each iteration consisted of
more than eight million power data points as the power metric
is recorded at a sample rate of 5 KHz. The Complementary
Cumulative Distribution Function (CCDF) plots for the power
consumption for the mentioned experiments are depicted in
Fig. 2. We classified the three energy states intuitively and
showed with a black solid line.
After measurement of the power consumption patterns
during the network activities, we developed the ExpCO2
software that can log a set of metrics corresponding to the
user smartphone interaction.

2013 22nd ITC Specialist Seminar on Energy Efficient and Green Networking (SSEEGN)

B. ExpCO2 Software and Power Measurements
ï1

Together with the ExpCO2’s metric recording functionality,
we added the feature that toggles the data state of the cellular
radio module between ON and OFF states with different time
intervals during screen-OFF state. ExpCO2’s aim is to limit
the occasional network traffic activities of all applications on
user smartphones, in a way that they will be activated at once
for a particular period of time, and to assess its influence
on the end-user perceived quality. Our proposal is that the
occasional network activities can be reduced and grouped in
a particular time window, which will minimize the number of
toggling between the states by different running applications
and services. Reducing the total number of network activities
will reduce the total number of cellular data state transitions,
the amount of inactivity timeout periods of the cellular radio,
and eventually the total energy consumption of the smartphone.
ExpCO2 does this preliminarily by disabling and enabling
the cellular data module completely for specific period of
time. We refer to TDataON and TDataOFF to DataON (dataenabled) and DataOFF (data-disabled) durations, respectively.
Towards this aim, we launched a set of cloud-based applications such as Facebook, Twitter, LinkedIn, Instagram, GMail,
GTalk, Google Services, WhatsApp from a dedicated user
application account. We also launched one Swedish news
application (AftonBladet [21]) that also has polling functionality. During the execution of the app, the ExpCO2’s extra
power consumption characteristics are investigated as well.
We first verify the power consumption of ExpCO2 with in-lab
measurements as a ground truth. Next, we conduct a subjective
study on users’ daily life natural environments; we employed
ExpCO2 on the users personal smartphones.
For the verification of power consumption of ExpCO2,
we conducted a set of ground truth energy measurements
in the lab with different scenarios via MPMT during the
screen-OFF state: (1) TDataOFF = 3 min; (2) TDataOFF =
9 min; (3) TDataOFF = 14 min; (4) TDataOFF = 19 min;
(5) TDataOFF = 29 min; (6) OFF, TDataOFF = TScreenOFF ,
i.e., data-disabled period is equal to the screen-OFF period
as parameterized by user; (7) NoToggling, i.e., with ExpCO2
but without toggling cellular data interface; (8) w/o ExpCO2,
i.e., without listening on sensors, without writing metrics to
the smartphone’s local storage, and without toggling cellular
data interface to obtain the base-line power consumption. We
collected samples for the eight different scenarios to such
extent that each scenario has a Standard Error (SE) of the mean
values less than 1 %. The Standard Error (SE) of the sample
mean for each scenario is calculated as: the standard deviation

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10

CCDF

The main purpose of ExpCO2 is to save smartphone
energy, when it is not interactively used by the user, particularly in screen-OFF state. Upon particular events, ExpCO2 logs various parameters: screen state (ON, OFF); cellular
data state (ON, OFF); the connected network type (3G, WiFi);
unique smartphone device ID; remaining battery level percent;
battery state (plugged, unplugged to/from power source); the
received application notifications (only corresponding package
name); user feedback (free text); and the five-scale Mean Opinion Score (MOS) (1 is bad and 5 is excellent) [22] together with
the impairment (1 is very annoying and 5 is imperceptible). The
ExpCO2 services, which collect the metrics, are running in the
background.

ï2

10

w/o ExpCO2. (SE = 0.05)
ExpCO2 : NoToggling. (SE = 0.07)
ExpCO2 : TDataOFF = 3 minutes; TDataON = 1 minute. (SE = 0.09)
ExpCO2 : TDataOFF = 9 minutes; TDataON = 1 minute. (SE = 0.08)
ExpCO2 : TDataOFF = 14 minutes; TDataON = 1 minute. (SE = 0.05)
ExpCO2 : TDataOFF = 19 minutes; TDataON = 1 minute. (SE = 0.05)
ExpCO2 : TDataOFF = 29 minutes; TDataON = 1 minute. (SE = 0.02)

ï3

10

ExpCO2 : TDataOFF = TScreenOFF. (SE = 0.02)
1

2

10

10

Power Consumption (mW)

Fig. 3.
Power consumption patterns for different available cellular data
durations in Screen-OFF state.

of the sample data divided by the square root of the number
of the samples. The CCDF plots are presented together with
the Standard Errors for each scenario in Fig. 3. In addition, we
calculate the Gain Factors for each scenario (over 10 million
samples each), while taking the w/o ExpCO2 scenario as baseline and is calculated in Eq. 1. P¯w/o ExpCO2 is the average
power consumption without ExpCO2; and P¯Scenario is the
average power consumption of any of the eight scenarios.
Therefore, the larger the gain factor, the higher the saved
energy.
P¯w/o ExpCO2
(1)
Gain Factor =
P¯Scenario
V.

I N -L AB E XP CO2 R ESULTS

The Gain Factors obtained through in-lab measurements
for different DataOFF durations is presented in Table I. The
TABLE I.
w/o ExpCO2
1

T HE G AIN FACTORS FOR DIFFERENT SCENARIOS .

NoToggling
0.34

3 m OFF
0.35

9 m OFF
0.54

14 m OFF
0.63

19 m OFF
0.83

29 m OFF
1.34

OFF
2.57

Gain Factor is less than 1 for the scenarios where the TDataOFF
duration is set to less than 29 min. One reason is ExpCO2’s
itself extra power consumption, which is likely caused by the
logging (listening and writing) process, i.e., without the latter,
the energy consumption is expected to decrease. Based on the
in-lab experiments, TDataOFF = 29 min and TDataON = 1 min
combination has a Gain Factor of 1.34, and expected to be
even more when the energy consuming logging process is
disabled. By taking the results obtained from this part as basis,
we conducted a user study to identify the influence of the
(29 min DataOFF, 1 min DataON) combination on the end-user
perceived quality during the smartphone’s screen-OFF state.
VI.

U SER S TUDY WITH E XP CO2

The ExpCO2 has two main phases in the user study: (i) Introduction Period (min. 2 days), (ii) Actual User Study Period.
The Introduction Period is important for us to construct a baseline on the end-user’s interaction with the smartphone in real
life, e.g., user feedback, screen state changes, received notifications from the cloud-based apps, the battery levels, whether the
smartphone is disconnected from the power source. During the
Introduction Period, there was no modification on the cellular
data flow of the user smartphone, however ExpCO2 recorded
metrics and wrote the logs into the local storage of the user

2013 22nd ITC Specialist Seminar on Energy Efficient and Green Networking (SSEEGN)

smartphones. During the Actual Study Period, the functionality
of ExpCO2 has changed such that the cellular data is enabled
as long as the screen is ON. Once the screen is turned OFF,
the cellular data is disabled automatically. ExpCO2 applied
TDataOFF to 29 min, and the TDataON to 1 min, which means
the data is enabled again after 29 min screen-OFF duration
and then enabled again only for one minute; next the data
is disabled again for another 29 min. This cycle continues
until the user turns ON the screen manually. When screen is
ON, the user is able to submit a user feedback or MOS on
spot anytime via Experience Sampling Method (ESM) [16].
We provided a user-friendly ESM interface for the subjects
along experience from pervious studies [1]. The participants,
who own Android smartphones, are chosen through an online
survey. Once the user reads consent and confirms participation
via the online form, the user is allowed to install ExpCO2
on their smartphones. We asked users to submit MOS, user
feedback, and any impairment during the study period, as soon
as they appear. We deliberately did not tell the users about the
network traffic change of the ExpCO2, or about the battery life
of their smartphone to prevent bias. After the study, in the final
interview with the users, we obtained more detailed knowledge
on their experiences. We asked to the users questions regarding
the battery life and the overall user perceived quality of the
smartphone. This interview and the feedback obtained through
ESM helped us to address the question “Do the users really
need to receive the notifications when the smartphone is not
used interactively?” If not, we may conclude that maximizing
the data-disabled duration can save energy without influencing
end-user QoE. The data analysis relies on the user feedback,
and the battery levels. The battery discharge curve depends on
factors such as the battery age and temperature [18]; however
we assumed that this did not change significantly during the
subjective study.
VII.

U SER S TUDY R ESULTS

As we have conducted the user study with five users
and three phone types so far, we did not run any statistical
significance tests, however, the results obtained are highly
indicative. The subjects were reluctant to give MOS ratings,
and when they were asked the reason they claimed that they
did not experience any difference, and it was perceived by
the users that the smartphone was functioning as in daily
life. Therefore, we focused on the qualitative feedback rather
than the quantitative ones. The data collected during the
Introduction Period and the Actual Study Period are presented
as different sections for each subject, S, in Table II. All 5 users
in the study have used GMail, GTalk, WhatsApp, and Viber.
Additional user apps are presented in Table II. The median
data- OFF/ON duration during screen-OFF were close to the
nominal (29 min/1 min) values: 29 min/1 min; 29 min/1.25 min;
26 min/2.2 min; 25 min/1 min for the subjects S1-to-S4, respectively. All of the users replied “No” to the question:
“Have you realized any change, e.g., regarding the notifications, when your smartphone screen was OFF?”. When asked
to the users: “Can you prioritize the cloud-based apps that
you really want to be notified?”, S2 replied: “WhatsApp,
LinkedIn, Email, Facebook, Twitter”. Based on the ExpCO2
logs, and the prioritization of the apps, S2 has used WhatsApp
frequently (received 422 WhatsApp notifications in 119 hours),
but the subject did not complain about any notification latency.

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TABLE II.
S1 - Samsung Galaxy Note2
TStudy = 116 hrs
TScreenOFF (min)
TScreenON (min)
Battery Level (%)
Used Apps
S1 - Samsung Galaxy Note2
TStudy = 75 hrs
TDataON (min)
TDataOFF (min)
Battery Level (%)
Total WiFi Duration (hours)
DeviceOFF Duration (hours)
Feedback
S2 - Samsung Galaxy S3
TStudy = 119 hrs
TScreenOFF (min)
TScreenON (min)
Battery Level (%)
Used Apps
S2 - Samsung Galaxy S3
TStudy = 75 hrs
TDataON (min)
TDataOFF (min)
Battery Level (%)
Total WiFi Duration (hours)
DeviceOFF Duration (hours)
Feedback
S3 - Sony Xperia
TStudy = 50 hrs
TScreenOFF (min)
TScreenON (min)
Battery Level (%)
Used Apps
S3 - Sony Xperia
TStudy = 43 hrs
TDataON (min)
TDataOFF (min)
Battery Level (%)
Total WiFi Duration (hours)
DeviceOFF Duration (hours)
Feedback
S4 - Samsung Galaxy S3
TStudy = 110 hrs
TScreenOFF (min)
TScreenON (min)
Battery Level (%)
Used Apps
S4 - Samsung Galaxy S3
TStudy = 30 hrs
TDataON (min)
TDataOFF (min)
Battery Level (%)
Total WiFi Duration (hours)
DeviceOFF Duration (hours)
Feedback

Mean
22
2.6
48

Mean
36.2
58
74

Mean
7.7
3.2
42

Mean
42
193
40

Mean
10.5
2.3

Introduction Period (without manipulating the cellular data)
Median
Std.
Size (Detected Events)
Model
4.2
64.4
279
Exp.
0.5
5.5
279
Exp.
47
26
7621
CloudMe, Verisure, Skype, LinkedIn, Shoutcast
Actual Study Period (with manipulating the cellular data)
Median
Std.
Size (Detected Events)
Model
1
106
37
29
164.5
36
79
20
5363
1.45
0
“it was normal.” Not realised any difference.
Introduction Period (without manipulating the cellular data)
Median
Std.
Size (Detected Events)
Model
1.6
23.5
656
Exp.
0.6
18.2
655
Exp.
36
30
15783
Facebook, Instagram, LinkedIn, Twitter
Actual Study Period (with manipulating the cellular data)
Median
Std.
Size (Detected Events)
Model
1.25
78.5
13
29
444
13
32
31
8692
2.6
21
Not realized any difference
Introduction Period (without manipulating the cellular data)
Median
Std.
Size (Detected Events)
Model
1.7
36
235
Exp.
0.5
12.7
234
Exp.

R2
0.93
0.96
-

R2
-

R2
0.95
0.93
-

R2
-

R2
0.92
0.96

46
46
24
3730
LinkedIn, Skype, Facebook, Facebook Messenger, Twitter, News/Sports app
Mean
38
32
55

Mean
32
5.43
48

Mean
51
18
60

S5 - Samsung Galaxy S3
S5 - Samsung Galaxy S3
Used Apps
TStudy = 62hrs
TScreenOFF (min)
TScreenON (min)
TDataON (min)
TDataOFF (min)
Total WiFi Duration (hours)
DeviceOFF Duration (hours)
Feedback

U SER S TUDY R ESULTS

Actual Study Period (with manipulating the cellular data)
Median
Std.
Size (Detected Events)
Model
2.2
91.5
37
26
49
37
54
27
1582
0.16
9
Not realised any difference.
Introduction Period (without manipulating the cellular data)
Median
Std.
Size (Detected Events)
Model
4.9
84
177
Exp.
0.25
29.3
176
Exp.
47
29
5737
Anydo, Facebook, Facebook Messenger, Twitter, Skype
Actual Study Period (with manipulating the cellular data)
Median
Std.
Size (Detected Events)
Model
1
214
18
25
13
19
71
26
1596
0
10
Not realised any difference.

R2
-

R2
0.92
0.89
-

R2
-

Introduction Period (without manipulating the cellular data)
Skipped for this user due to technical issues

Mean
7.9
1.6
138
144

Actual Study Period (with manipulating the cellular data)
Using Facebook through the browser
Std.
Size (Detected Events)
Model
22.4
391
Exp.
3.3
391
Exp.
421
12
421
12
0
0
“I do not want to get notifications. Notifications for example
for Facebook would probably stress the life out of me
and are absolutely switched off on my phone”
Median
1.2
0.3
0.4
0.3

R2
0.93
0.91
-

This might be due to the fact that the user expectation to
receive a notification decreases when the user turns the screenOFF, which needs to be investigated in more detail with
more subjects. Some users interact with their smartphones
frequently, while some do it less often such as the difference
in the number of screen-ON/OFF toggles for S1 (279 times in
116 hours) and S2 (656 times in 119 hours). We assumed that
the probability distribution of screen- ON and OFF durations
is memoryless, i.e., user’s current screen- ON/OFF duration
does not depend on the users’ previous screen- ON/OFF
durations. Thereby, we fit the screen- ON/OFF durations to an
exponential curve. We show that, for the subjects S1-S5, the
screen- ON/OFF durations are exponentially distributed during

2013 22nd ITC Specialist Seminar on Energy Efficient and Green Networking (SSEEGN)

the Introduction Period (R2 (Coefficient of Determination) ≥
0.91) with median durations 0.5 min/4.2 min, 0.6 min/1.6 min,
0.5 min/1.7 min, 0.25 min/4.9 min, 0.3 min/1.2 min, respectively. While the smartphone was running as being disconnected from the power source, the mean battery levels have
increased by 54 %, 20 %, and 26 %, for S1, S3, and S4, respectively during the Actual Study as compared to the Introduction
Period. S2 has used 2.6 hours of WiFi and the smartphone was
completely OFF for 21 hours, therefore the results belong to
S2 are not indicative to understand the improvement in energy
levels for this particular user. Thereby, we focus on qualitative
analysis based on any possible perceived quality degradation.
Although, we cannot claim statistical significant conclusions
due to rather high standard error values, the results are highly
indicative.
VIII.

D ISCUSSION AND L IMITATIONS

During the Actual User Study, subject S1 wasn’t able to
listen to Internet radio while driving. Therefore, we added
an extra feature to ExpCO2 so that the radio was kept ON
even when the screen is OFF based on the user preference.
But, we asked the user to disable that feature after driving. In
addition, the battery saving for S5 is unknown, as there were
technical problems during the Introduction Period. Based on
the qualitative results of S5, the subject has not realized any
degradation in the perceived quality on the cloud-based apps
during the Actual Study Period.

R EFERENCES
[1]

[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]

[11]

[12]
[13]

[14]
[15]
[16]
[17]

C ONCLUSIONS AND F UTURE W ORK

In this paper, we present the power consumption patterns of
Samsung Galaxy S smartphone during a set of data network
activities on the cellular radio, and then developed ExpCO2
that enables or disables the cellular data at predefined intervals, while extending the non-Idle periods. ExpCO2 reduced
asynchronous network activities of individual cloud-based apps
and let them start network activities simultaneously during
the 1 min DataON period. We showed that the energy gain
of such solution (called ExpCO2) increases with the duration of DataOFF periods during screen-OFF state. Based on
the obtained quantitative results through in-lab experiments,
a DataON/DataOFF cycle with 29 min DataOFF, and 1 min
DataON duration during screen-OFF state indicated a 34%
energy gain. We confirmed with the qualitative subjective study

978-1-4799-0977-3/13/$31.00 ©2013 IEEE

The indicative results acquired in this paper have potential for future work. We will investigate the network traffic,
conduct a user study with more users, and tune the DataON
and DataOFF durations based on user-smartphone interaction
requirements in terms of notification latency. We encourage
the cloud-based Android app developers to use the AlarmManager [24] in Android API, so that the apps can broadcast
the network activity events to the other listening apps, which
could let all the apps to synchronize their network activities
in an autonomous and reliable manner, and save energy.

[2]

ExpCO2’s power saving depends on human usersmartphone interaction. We recommend ExpCO2 for the users
who check emails less frequent than every 29 min to save
energy. In addition, ExpCO2 might change user behaviour,
particularly for those users who receive too many notifications
and being interrupted by them often in daily life. For those
users, ExpCO2 provides an extra benefit such as reduced
interruption by notifications of e.g., cloud-based apps. Another
drawback of frequent interruption during screen-OFF state is
that, each interruption possibly causes users to turn the screen
ON each time, which also increases the power consumption related to other components, e.g., backlight display. As ExpCO2
merely controls cellular data only during screen-OFF state, it
is suggested for particular users whose smartphone interaction
is based on the received app notifications; ExpCO2 might help
them change their user smartphone interaction in a good way,
being less interrupted (save time), and in parallel save energy.

IX.

that the user perceived quality is not degragaded with this
DataON/DataOFF duration combination.

18

[18]
[19]
[20]
[21]
[22]
[23]

[24]

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